Online Game Emulation Platform – Afterplay.io

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Hello HN users, I made a web app that allows you to upload any SNES or GameBoy ROM file and play right within the web browser. It’s called Afterplay.io. It got a lot of upvotes on Reddit, and I noticed users signing up and coming back to play games every day, which feels great and has motivated me to keep on developing this thing :). Demo Video : https://www.youtube.com/watch?v=3K9iYbJ-1lQ Site : https://afterplay.io Problem: I couldn't play emulated games across my devices easily with saves. The Apple App Store does not allow emulator apps. Solution: Use Afterplay.io. It works in the browser, on PC’s, Mac’s, Android and iOS phones. Once you upload a game file, it will be stored on our server and we save the state of your gameplay automatically each 20 seconds. You can sign in, upload and play a game from your Mac, and later resume from your iPad or Android phone right where you left off. This is the first iteration of the web app. The core functionality is working but there’s still a lot of work to do. So I’m writing code and pushing tiny improvements a few times a day. I welcome any thoughts or suggestions.

Visit product View on Hacker NewsMay 18, 2021

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, apple, apps · Missing: agents, macos, agent

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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